Related Experiment Video
Updated: Jan 17, 2026
05:02
Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
33.5K
Bibliometric Analysis of Surgical Articles Using Bayesian Statistics.
Zhenyu Li1,2, Aliya Izumi2, Dominique Vervoort3,4
1From the Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Summary
Bayesian statistics use in surgical research is growing, particularly in observational studies and meta-analyses. Further standardization of Bayesian reporting is crucial for enhancing transparency and reproducibility in surgical studies.
Area of Science:
- Surgical Research
- Biostatistics
- Medical Informatics
Background:
- Traditional surgical research primarily uses frequentist methods.
- Bayesian statistics offers advantages like incorporating prior evidence and flexible uncertainty modeling.
- The application of Bayesian methods in high-impact surgical literature is not well-documented.
Purpose of the Study:
- To analyze trends in Bayesian statistics adoption in high-impact surgical publications over two decades.
- To characterize studies employing Bayesian methods in surgery.
- To evaluate the quality of Bayesian analysis reporting in surgical research.
Main Methods:
- A systematic review of surgical articles from high-impact journals (Web of Science, PubMed) from 2000-2024.
- Bibliometric and content analysis of retrieved articles.
- Assessment of Bayesian reporting quality using the Reporting of Bayes Used in Clinical Studies (ROBUST) scale.
Main Results:
- 120 articles met the inclusion criteria, showing a 12.3% annual growth in Bayesian statistics use.
- General surgery and cardiothoracic surgery were the most represented specialties.
- Retrospective cohort studies and meta-analyses were common designs; regression-based methods were most frequent. Average ROBUST score was 4.1/7, with 54% specifying priors.
Conclusions:
- Bayesian statistics are increasingly utilized in surgical research, especially in observational studies and meta-analyses.
- While adoption is rising, there's a need for improved quality and standardization in Bayesian reporting.
- Enhancing reporting quality will boost transparency and reproducibility in Bayesian surgical research.
More Related Videos
07:50Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
Published on: April 18, 2025
874
07:41Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
9.5K
Related Concept Videos
02:46Chi-square Analysis
43.6K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
43.6K
01:21Cancer Survival Analysis
648
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
648
01:24Kaplan-Meier Approach
577
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
577
01:25Statistical Methods for Analyzing Epidemiological Data
898
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
898
01:20Biostatistics: Overview
732
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...
732
01:20Comparing the Survival Analysis of Two or More Groups
561
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
561